Identifying a type of motion of an object

ABSTRACT

An apparatus for identifying a type of motion and condition of a user is disclosed. One apparatus includes a motion detection sensor operative to generate an acceleration signature based on sensed acceleration of the user, and a controller. The controller is operative to determine what network connections are available to the motion detection device, match the acceleration signature with at least one of a plurality of stored acceleration signatures, wherein each stored acceleration signatures corresponds with a type of motion of the user, wherein the apparatus distributes at least some of the acceleration signature matching processing when processing capability is available to the motion detection device though available network connections, and identify the type of motion of the user and identify a condition of the user based on the matching of the acceleration signature.

RELATED APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 12/883,304, filed Sep. 16, 2010, which is a continuation inpart (CIP) of U.S. patent application Ser. No. 12/560,069 filed on Sep.15, 2009, which claims priority to U.S. provisional patent applicationSer. No. 61/208,344 filed on Feb. 23, 2009 which are all incorporated byreference.

FIELD OF THE DESCRIBED EMBODIMENTS

The described embodiments relate generally to motion detecting. Moreparticularly, the described embodiments relate to a method and apparatusfor identifying a type of motion of an animate or inanimate object.

BACKGROUND

There is an increasing need for remote monitoring of individuals,animals and inanimate objects in their daily or natural habitats. Manyseniors live independently and need to have their safety and wellnesstracked. A large percentage of society is fitness conscious, and desireto have, for example, workouts and exercise regimen assessed. Publicsafety officers, such as police and firemen, encounter hazardoussituations on a frequent basis, and need their movements, activities andlocation to be mapped out precisely.

The value in such knowledge is enormous. Physicians, for example, liketo know their patients sleeping patterns so they can treat sleepdisorders. A senior living independently wants peace of mind that if hehas a fall it will be detected automatically and help summonedimmediately. A fitness enthusiast wants to track her daily workoutroutine, capturing the various types of exercises, intensity, durationand caloric burn. A caregiver wants to know that her father is living anactive, healthy lifestyle and taking his daily walks. The police wouldlike to know instantly when someone has been involved in a carcollision, and whether the victims are moving or not.

Existing products for the detection of animate and inanimate motions aresimplistic in nature, and incapable of interpreting anything more thansimple atomic movements, such as jolts, changes in orientation and thelike. It is not possible to draw reliable conclusions about humanbehavior from these simplistic assessments.

It is desirable to have an apparatus and method that can accuratelymonitor motion of either animate of inanimate objects.

SUMMARY

An embodiment includes a method of detecting human condition andactivity of a user. The method includes generating, by a motiondetection device that includes a motion detection sensor, anacceleration signature based on sensed acceleration of an object thatrepresents motion of the user, the motion detection device determiningwhat network connections are available to the motion detection device,matching the acceleration signature with at least one of a plurality ofstored acceleration signatures of motions of human beings, wherein eachstored acceleration signatures corresponds with a type of motion,wherein the motion detection device distributes at least some of theacceleration signature matching processing when processing capability isavailable to the motion detection device though available networkconnections, and identifying a type of motion of the user andidentifying a condition of the user based on the matching of theacceleration signature with a stored acceleration signature.

Another embodiment includes an apparatus for identifying a type ofmotion and condition of a user. The apparatus includes a motiondetection sensor operative to generate an acceleration signature basedon sensed acceleration of the user, and a controller. The controller isoperative to determine what network connections are available to themotion detection device, match the acceleration signature with at leastone of a plurality of stored acceleration signatures, wherein eachstored acceleration signatures corresponds with a type of motion of theuser, wherein the apparatus distributes at least some of theacceleration signature matching processing when processing capability isavailable to the motion detection device though available networkconnections, and identify the type of motion of the user and identify acondition of the user based on the matching of the accelerationsignature.

Other aspects and advantages of the described embodiments will becomeapparent from the following detailed description, taken in conjunctionwith the accompanying drawings, illustrating by way of example theprinciples of the described embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows examples of different types of motions of a human beingthat an object attached to the human being can be used to detect orsense.

FIGS. 2A, 2B, 2C shows examples of time-lines of several differentacceleration curves (signatures), wherein each signature is associatedwith a different type of sensed or detected motion.

FIG. 3 is an example of a block diagram of a motion detection device.

FIG. 4 is a flowchart that includes the steps of an example of a methodfor detecting various motions of daily living activities and emergencysituations, such as, a fall.

FIG. 5 is a flowchart that includes the steps of a method for detectionof a fall.

FIG. 6 is a flow chart that includes the steps of one example of amethod of identifying a type of motion of an animate or inanimateobject.

FIG. 7 is a flow chart that includes steps of one example of a method ofa motion detection device checking network availability for improvementsin speed and/or processing power of acceleration signature matching.

FIG. 8 shows a motion detection device that can be connected to one ofmultiple networks.

DETAILED DESCRIPTION

The monitoring of human activities generally falls into threecategories: safety, daily lifestyle, and fitness. By carefullyinterpreting human movements it is possible to draw accurate andreasonably complete inferences about the state of well being ofindividuals. A high degree of sophistication is required in theseinterpretations. Simplistic assessments of human activity lead toinaccurate determinations, and ultimately are of questionable value. Bycontrast, a comprehensive assessment leads to an accurate interpretationand can prove to be indispensable in tracking the well being and safetyof the individual.

To draw accurate inferences about the behavior of humans, it turns outthat the atomic movements become simply alphabets that include elementalmotions. Furthermore, specific sequences of elemental motions become thevocabulary that comprises human behavior. As an example, take the caseof a person who leaves the home and drives to the shopping center. Insuch a scenario, the behavioral pattern of the person is walking to thedoor or the house, opening and closing the door, walking further to thecar, settling down in the car, starting the engine, accelerating thecar, going through a series of stops, starts and turns, parking the car,getting out and closing the car door, and finally walking to theshopping center. This sequence of human behavior is comprised ofindividual motions such as standing, walking, sitting, accelerating (inthe car), decelerating, and turning left or right. Each individualmotion, for example walking, is comprised of multiple atomic movementssuch as acceleration in an upward direction, acceleration in a downwarddirection, a modest forward acceleration with each step, a modestdeceleration with each step, and so on.

With written prose, letters by themselves convey almost no meaning atall. Words taken independently convey individual meaning, but do notprovide the context to comprehend the situation. It takes a completesentence to obtain that context. Along the same line of reasoning, itrequires a comprehension of a complete sequence of movements to be ableto interpret human behavior.

Although there is an undeniable use for products that are able to detectcomplex human movements accurately, the key to the success of suchtechnologies lies in whether users adopt them or not. The technologyneeds to capture a wide range of human activities. The range ofmovements should ideally extend to all types of daily living activitiesthat a human being expects to encounter—sleeping, standing, walking,running, aerobics, fitness workouts, climbing stairs, vehicularmovements, falling, jumping and colliding, to name some of the morecommon ones.

It is important to detect human activities with a great deal ofprecision. In particular, activities that relate to safety, fitness,vehicular movements, and day to day lifestyle patterns such as walking,sleeping, climbing stairs, are important to identify precisely. Forexample, it is not enough to know that a person is walking One needs toknow the pace and duration of the walk, and additional knowledge ofgait, unsteadiness, limping, cadence and the like are important.

It is critical that false positives as well as false negatives beeliminated. This is especially important for cases of safety, such asfalls, collisions, and the like. Human beings come in all types—short,tall, skinny, obese, male, female, athletic, couch potato, peoplewalking with stick/rolator, people with disabilities, old and young. Theproduct needs to be able to adapt to their individuality and lifestyle.

The embodiments described provide identification of types of motion ofan animate or inanimate object. Motion is identified by generatingacceleration signatures based on the sensed acceleration of the object.The acceleration signatures are compared with a library of motionsignature, allowing the motion of the object to be identified. Further,sequences of the motions can be determined, allowing identification ofactivities of, for example, a person the object is attached to.

Just as the handwritten signatures of a given human being aresubstantively similar from one signature instance to the next, yet haveminor deviations with each new instance, so too will the motionsignatures of a given human be substantively similar from one motioninstance to the next, yet have minor deviations.

Algorithms used for pattern recognition (signature matching) should havethe sophistication to accurately handle a wide range of motions. Suchalgorithms should have the ability to recognize the identicalcharacteristics of a particular motion by a given human being, yet allowfor minor variations arising from human randomness. Additionally, thedevices used to monitor peoples' movement need to be miniature and easyto wear. These two objectives are fundamentally opposed. However, thedescribed embodiments provide a single cohesive system that is bothsophisticated enough to detect a wide range of motions.

FIG. 1 shows examples of different types of motions of a human beingthat an object attached to the human being can be used to detect orsense. The human motions can include, for example, standing, sleeping,walking, and running A first motion 110 can include walking A secondmotion 120 can include falling. A third motion 130 can include runningEach of the motions generates a unique motion signature. As will bedescribed, the signatures can be universal to, for example, manyindividuals. Additionally, the signatures can have additionalcharacteristics that are unique to an individual.

FIGS. 2A, 2B, 2C shows examples of different types of acceleration andorientation signatures for various sample motions by human beings. Itshould be noted that these signatures are expected to have certaincomponents that are common from one human being to the next, but alsohave certain components that vary from one human to the next.

The signatures of FIGS. 2A, 2B, 2C are depicted in only one orientation.That is, three accelerometers can be used to generate accelerationsignatures in the X, Y and Z (three) orientations. The signatures ofFIGS. 2A, 2B, 2C only show the signature of one of the threeorientations. It is to be understood that matching can use the otherorientations as well.

FIG. 2A shows an example of an acceleration signature of a person doinga slow fall and lying down summersault. FIG. 2B shows an example of anacceleration signature of a person slipping and falling back on a bouncysurface (for example, an air mattress). FIG. 2C shows an accelerationsignature of a person fall on their face with their knees flexed. Bymatching an acceleration signature that has been generated by sensingthe motion of a person with one of many stored signatures, the motion ofthe person can be determined.

FIG. 3 is an example of a block diagram of a motion detection device.The motion detection device can be attached to an object, and therefore,detect motion of the object that can be identified. Based on theidentified motion, estimates of the behavior and conditions of theobject can be determined.

The motion detection device includes sensors (such as, accelerometers)that detect motion of the object. One embodiment of the sensors includesaccelerometers 312, 314, 316 that can sense, for example, accelerationof the object in X, Y and Z directional orientations. It is to beunderstood that other types of motion detection sensors canalternatively be used.

An analog to digital converter (ADC) digitizes analog accelerometersignals. The digitized signals are received by compare processingcircuitry 330 that compares the digitized accelerometer signals withsignatures that have been stored within a library of signatures 340.Each signature corresponds with a type of motion. Therefore, when amatch between the digitized accelerometer signals and a signature storedin the library 340, the type of motion experienced by the motiondetection device can determined.

An embodiment includes filtering the accelerometer signals beforeattempting to match the signatures. Additionally, the matching processcan be made simpler by reducing the possible signature matches.

An embodiment includes identifying a previous human activity context.That is, for example, by knowing that the previous human activity waswalking, certain signatures can intelligently be eliminated from thepossible matches of the present activity that occurs subsequent to theprevious human activity (walking).

An embodiment includes additionally reducing the number of possiblesignature matches by performing a time-domain analysis on theaccelerometer signal. The time-domain analysis can be used to identify atransient or steady-state signature of the accelerometer signal. Thatis, for example, a walk may have a prominent steady-state signature,whereas a fall may have a prominent transient signature. Identificationof the transient or steady-state signature of the accelerometer signalcan further reduce or eliminate the number of possible signaturematches, and therefore, make the task of matching the accelerometersignature with a signature within the library of signature simpler, andeasier to accomplish. More specifically, the required signal processingis simpler, easier, and requires less computing power.

Upon detection of certain types of motion, an audio device 360 and/or aglobal positioning system (GPS) 370 can engaged to provide additionalinformation that can be used to determine the situation of, for example,a human being the motion detection device is attached to.

The condition, or information relating to the motion detection devicecan be communicated through a wired or wireless connection. A receiverof the information can process it, and make a determination regardingthe status of the human being the motion detection device is attachedto.

FIG. 4 is a flowchart that includes the steps of an example of a methodfor detecting various motions of daily living activities and emergencysituations, such as, a fall. A first step 410 includes monitoring anactivity of a person the motion detection device is attached. Raw signaldata is collected from, for example, an accelerometer sensor. A secondstep 420 includes performing instantaneous computations over raw signalsto compute atomic motions along with gravity vector and tilt vector. Astep third step 430 includes applying series of digital filters toremove noise in the atomic motions data. A fourth step 440 includesperforming state analysis on series of atomic data samples and formingcontext. Depending on the state analysis, the series of atomic data ispassed through either a step 445 periodic or steady state data analysisor a step 450 transient state data analysis. A sixth step 460 includesformation of macro motion signatures. The macro motion signatures arebuilt from an output of state analysis vectors using known wavelettransformation techniques (for example, a Haar Transform). The transformperforms pattern matching on current motion pattern with existing motionpattern library using, for example, DWT (Discreet Wavelet Transform)techniques. Complex motion wavelets are later matched using statisticalpattern matching techniques, such as, HHMM (Hidden Heuristic MarkovModel). The statistical pattern matching includes detecting andclassifying events of interest. The events of interest are built byobserving various motions and orientation states data of an animate orinanimate object. This data is used to train the statistical model whichperforms the motion/activity detection. Each activity will have its ownmodel trained based on the observed data. A seventh step 470 includes alearning system providing the right model for the user from a set ofmodel. It also aids in building newer (personal) patterns which are notin the library for the person who is wearing the motion detectiondevice. An eighth step 480 includes pre-building a motion database ofmotion libraries against which motion signatures are compared. Thedatabase adds new motion/states signature dynamically as they areidentified.

FIG. 5 is a flowchart that includes the steps of an example of a methodfor detecting a fall. A first step 510 includes monitoring an activityof, for example, a person the motion detection device is attached to. Astep 515 includes recording and reporting in deviations in normal motionpatterns of the person. A step 520 includes detecting the accelerationmagnitude deviation exceeding a threshold. The acceleration magnitudedeviation exceeding the threshold can be sensed as a probable fall, andaudio recording is initiated. Upon detection of this condition, soundrecording of the person the motion detection device is connected to canbe activated. The activation of sound can provide additional informationthat can be useful in assessing the situation of the person. A step 530includes monitoring the person after the probable fall. A step 525includes detection of another acceleration having magnitude lesser thanthe threshold, and continuing monitoring of audio. A step 535 includesdetecting a short period of inactivity. A step 540 includes monitoringthe person after determining a fall probably occurred. A step 545includes subsequently detecting normal types of motion and turning offthe audio because the person seems to be performing normal activity. Astep 550 includes monitoring a period of inactivity. A step 555 includesadditional analysis of detected information and signals. A step 560includes further analysis including motion data, orientation detectionall indicating the person is functioning normally. A step 560 includesdetermining that a fall has occurred based on the analysis of the motiondata, and analysis of a concluded end position and orientation of theperson. The sound recording can be de-activated. A step 565 includesconcluding that a fall has occurred. A step 570 includes sending analert and reporting sound recordings. A step 575 includes the fallhaving been reported. A step 580 includes an acknowledgement of thefall.

FIG. 6 is a flow chart that includes the steps of one example of amethod of identifying a type of motion of an animate or inanimateobject. A first step 610 includes generating an acceleration signature(for example, a tri-axial) based on the sensed acceleration of theobject. A second step 620 includes matching the acceleration signaturewith at least one of a plurality of stored acceleration signatures,wherein each stored acceleration signatures corresponds with type ofmotion. A third step 630 includes identifying the type of motion of theobject based on the statistical (pattern) matching or exact matching ofthe acceleration signature. As will be described, the accelerationsignal can be created using a wavelet transformation.

For embodiments, the type of motion includes at least one of atomicmotion, elemental motion and macro-motion.

Though embodiments of generating matching acceleration signatures aredescribed, it is to be understood that additional or alternateembodiments can include generating and matching of orientation and/oraudio signatures. Correspondingly, the first step 610 can includegenerating an acceleration signature, (and/or) orientation and audiosignature based on the sensed acceleration, orientation of the objectand audio generated by the object, for example, a thud of a fall, or acry for help.

Atomic motion includes but is not limited to a sharp jolt, a gentleacceleration, complete stillness, a light acceleration that becomesstronger, a strong acceleration that fades, a sinusoidal orquasi-sinusoidal acceleration pattern, vehicular acceleration, vehiculardeceleration, vehicular left and right turns, and more.

Elemental motion includes but is not limited to motion patterns forwalking, running, fitness motions (e.g. elliptical machine exercises,rowing, stair climbing, aerobics, skipping rope, bicycling . . . ),vehicular traversal, sleeping, sitting, crawling, turning over in bed,getting out of bed, getting up from chair, and more.

Macro-motion includes but is not limited to going for a walk in thepark, leaving home and driving to the shopping center, getting out ofbed and visiting the bathroom, performing household chores, playing agame of tennis, and more.

Each of the plurality of stored acceleration signatures corresponds witha particular type of motion. By matching the detected accelerationsignature of the object with at least one of a plurality of storedacceleration signatures, an estimate or educated guess can be made aboutthe detected acceleration signature.

An embodiment includes a common library and a specific library, andmatching the acceleration signature includes matching the accelerationsignature with stored acceleration signatures of the common library, andthen matching the acceleration signature with stored accelerationsignatures of the specific library. For a particular embodiment, thegeneral library includes universal acceleration signatures, and thespecific library includes personal acceleration signatures. That is, forexample, the stored acceleration signatures of the common library areuseable for matching acceleration signatures of motions of multiplehumans, and the stored acceleration signatures of the specific libraryare useable for matching acceleration signatures of motions of aparticular human. Additionally, each library can be further categorizedto reduce the number of possible matches. For example, at aninitialization, a user may enter physical characteristics of the user,such as, age, sex and/or physical characteristics (such as, the user hasa limp). Thereby, the possible signatures matches within the generallibrary can be reduced. The signature entries within the specificlibrary can be learned (built) over time as the human wearing the motiondetection device goes through normal activities of the specific human.The specific library can be added to, and improved over time.

An embodiment includes filtering the acceleration signals. Additionalembodiment include reducing the number of stored acceleration signaturematches by identifying a previous activity of the object, and performinga time domain analysis on the filtered acceleration signal to identifytransient signatures or steady-state signatures of the filteredacceleration signal. That is, by identifying a previous activity (forexample, a human walking of sleeping) the possible number of presentactivities can be reduced, and therefore, the number of possible storedacceleration signature matches reduced. Additionally, the transientand/or steady-state signatures can be used to reduce the number ofpossible stored acceleration signature matches, which can improve theprocessing speed.

Another embodiment includes activating audio sensing of the object ifmatches are made with at least portions of particular storedacceleration signatures. For example, if the acceleration signatureexceeds a threshold value, then audio sensing of the object isactivated. This is useful because the audio information can provideadditional clues as to what, for example, the condition of a person.That is, a fall may be detected, and audio information can be used toconfirm that a fall has in fact occurred.

Another embodiment includes transmitting the sensed audio. For example,of a user wearing the object has fallen, and the fall has been detected,audio information can be very useful for determining the condition ofthe user. The audio information can allow a receiver of the audioinformation to determine, for example, if the user is in pain,unconscious or in a dangerous situation (for example, in a shower or ina fire).

An embodiment includes the object being associated a person, and thestored acceleration signatures corresponding with different types ofmotion related to the person. A particular embodiment includesidentifying an activity of the person based on a sequence of identifiedmotions of the person. The activity of the person can include, forexample, falling (the most important in some applications), walking,running, driving and more. Furthermore, the activities can be classifiedas daily living activities such as walking, running, sitting, sleeping,driving, climbing stairs, and more, or sporadic activities, such asfalling, having a car collision, having a seizure and so on.

An embodiment includes transmitting information related to theidentified type of motion if matches are made with particular storedacceleration signatures. The information related to the identified typeof motion can include at least one of motions associated with a personthe object is associated with. The motions can include, for example, aheartbeat of the person, muscular spasms, facial twitches, involuntaryreflex movements which can be sensed by, for example, an accelerometer.Additionally, the information related to the identified type of motioncan include at least one of location of the object, audio sensed by theobject, temperature of the object.

Another embodiment includes storing at least one of the plurality ofstored acceleration signatures during an initialization cycle. Theinitializing cycle can be influenced based on what the object isattached to. That is, initializing the stored acceleration signatures(motion patterns) can be based on what the object is attached to, whichcan both reduce the number of signatures required to be stored within,for example, the general library, and reduce the number of possiblematches and reduce the processing required to identify a match.Alternatively or additionally, initializing the stored accelerationsignatures can be based on who the object is attached to, which caninfluence the specific library. The initialization can be used todetermine motions unique, for example, to an individual. For example, aunique motion can be identified for a person who walks with a limp, andthe device can be initialized with motion patterns of the person walkingwith a limp.

An embodiment includes initiating a low-power sleep mode of the objectif sensed acceleration is below a threshold for a predetermined amountof time. That is, if, for example, a person is sensed to be sleeping,power can be saved by de-activating at least a portion of the motionsensing device.

FIG. 7 is a flow chart that includes steps of one example of a method ofa motion detection device checking network availability for improvementsin speed and/or processing power of acceleration signature matching,wherein the motion detection device includes motion detection sensorsthat generate the acceleration signal. A first step 710 includes themotion detection device determining what network connections areavailable to the motion detection device. A second step 710 includes themotion detection device distributing at least some of the accelerationsignature matching processing if processing capability is available tothe motion detection device though available network connections.

For an embodiment, the motion detection device distributes theacceleration signature matching processing if the processing capabilityis available to the motion detection device though available networkconnections, and distributing the acceleration signature matchingprocessing saves the motion detection device processing power. Anotherembodiment, the motion detection device distributes the accelerationsignature matching processing if the processing capability is availableto the motion detection device though available network connections, anddistributing the acceleration signature matching processing increases aspeed of the motion detection device processing. Alternatively, themotion detection device distributes the processing to optimize bothpower and processing speed. Additionally, the processing distributioncan be dependent upon the bandwidths of the available networkconnections. That is, some networks connections can generally supporthigher data transfer rates, and therefore, influence the processingspeed.

Generally, the motion detection device scales its processing to thelevel of processing available. That is, as additional processing powerbecomes available to the motion detection device, the motion detectiondevice can increase the complexity of the signature matching processing.The processing can be distributed as processing capability becomesavailable through network connections. The processing can be performedin different locations as network connectivity becomes available, whichcan advantageously reduce the power consumption of the motion detectiondevice and/or increase the speed of the processing.

FIG. 8 shows a motion detection device 300 that can be connected to oneof multiple networks. Examples of possible networks (not a comprehensivelist) the motion detection device 300 can connect to, include a cellularnetwork 820 through, for example, a blue tooth wireless link 810, or toa home base station 840 through, for example, a Zigbee wireless link845. The wireless links 810, 845 can each provide different levels ofbandwidth. Each of the networks includes available processingcapabilities 830, 850.

If the motion detection device 300 does not have any network connectionsavailable, the motion detection device 300 must perform its own matchingprocessing. If this is the case, then the processing algorithms may beless complex to reduce processing power, and/or reduce processing speed.For example, the matching processing can be made simpler by comparingthreshold levels for elemental motions by extracting significant waveletcoefficients. Acceleration signals data acquisition is performed inchunk of processing every few mili-seconds by waking up. For all othertimes the processor rests in low-power mode. Except for the emergencysituation, the RF communication is done periodically when the data is insteady state, there is no need to send it to network i.e. when theobject is in sedentary there is no need to send data change in the stateis communicated to network. Additionally, if no network connections areavailable, the operation of the motion detection device 300 may bealtered. For example, if the motion detection device 300 detects anemergency situation (such as, a fall), the motion detection device 300may generate an audio alert. If a network connection was available, theaudio alert may not be generated, but an alert may be transmitted overthe available network.

The motion detection device 300 includes a processor in which at least aportion of the analysis and signature matching can processing can becompleted. However, if the motion detection device 300 has one or morenetworks available to the motion detection device 300, the motiondetection device can off-load some of the processing to one of theprocessors 730, 750 associated with the networks.

The determination of whether to off-load the processing can be based onboth the processing capabilities provided by available networks, and thedata rates (bandwidth) provided by each of the available networks.

Although specific embodiments have been described and illustrated, theembodiments are not to be limited to the specific forms or arrangementsof parts so described and illustrated.

What is claimed:
 1. A method of detecting human condition and activity of a user, comprising: generating, by a motion detection device that includes a motion detection sensor, an acceleration signature based on sensed acceleration of an object that represents motion of the user; the motion detection device determining what network connections are available to the motion detection device; matching the acceleration signature with at least one of a plurality of stored acceleration signatures of motions of human beings, wherein each stored acceleration signatures corresponds with a type of motion, wherein the motion detection device distributes at least some of the acceleration signature matching processing when processing capability is available to the motion detection device though available network connections; identifying a type of motion of the user and identifying a condition of the user based on the matching of the acceleration signature with a stored acceleration signature.
 2. The method of claim 1, wherein the type of motion comprises at least one of atomic motion, elemental motion and macro-motion.
 3. The method of claim 1, wherein the stored acceleration signatures are stored in a common library and a specific library, and matching the acceleration signature comprises matching the acceleration signature with stored acceleration signatures of the common library, and then matching the acceleration signature with stored acceleration signatures of the specific library.
 4. The method of claim 3, wherein the common library includes universal motion and activities acceleration signatures, and the specific library includes person acceleration signatures.
 5. The method of claim 3, wherein the stored acceleration signatures of the common library are useable for matching acceleration signatures of motions of multiple humans, and the stored acceleration signatures of the specific library are useable for matching acceleration signatures of motions of a particular human.
 6. The method of claim 1, wherein when matches are made with at least portions of particular stored acceleration signatures, then audio sensing of the object is activated.
 7. The method of claim 1, wherein matching further comprises filtering the acceleration signature.
 8. The method of claim 7, further comprising reducing a number of stored acceleration signature matches by identifying a previous activity of the user, and performing a time domain analysis on the filtered acceleration signal to identify transient signatures or steady-state signatures of the filtered acceleration signal.
 9. The method of claim 1, wherein if the acceleration signature exceeds a threshold value, then audio sensing of the object is activated.
 10. The method of claim 9, further comprising transmitting the sensed audio.
 11. The method of claim 1, further comprising identifying an activity of the user based on a sequence of identified motions of the person.
 12. The method of claim 1, further comprising transmitting information related to the identified type of motion when matches are made with particular stored acceleration signatures.
 13. The method of claim 1, further comprising storing at least one of the plurality of stored acceleration signatures during an initialization cycle.
 14. The method of claim 1, further comprising initiating a low-power sleep mode of the object when sensed acceleration is below a threshold for a predetermined amount of time.
 15. The method of claim 1, wherein the motion detection device distributes the acceleration signature matching processing when the processing capability is available to the motion detection device through available network connections, and distributing the acceleration signature matching processing saves the motion detection device processing power.
 16. The method of claim 1, wherein the motion detection device distributes the acceleration signature matching processing when the processing capability is available to the motion detection device through available network connections, and distributing the acceleration signature matching processing increases a speed of the motion detection device processing.
 17. An apparatus for identifying a type of motion and condition of a user, comprising: a motion detection sensor operative to generate an acceleration signature based on sensed acceleration of the user; a controller, wherein the controller is operative to: determine what network connections are available to the motion detection device; match the acceleration signature with at least one of a plurality of stored acceleration signatures, wherein each stored acceleration signatures corresponds with a type of motion of the user, wherein the apparatus distributes at least some of the acceleration signature matching processing when processing capability is available to the motion detection device though available network connections; identify the type of motion of the user and identify a condition of the user based on the matching of the acceleration signature.
 18. The apparatus of claim 17, wherein the type of motion comprises at least one of atomic motion, elemental motion and macro-motion.
 19. The apparatus of claim 17, wherein the stored acceleration signatures are stored in a common library and a specific library, and matching the acceleration signature comprises matching the acceleration signature with stored acceleration signatures of the common library, and then matching the acceleration signature with stored acceleration signatures of the specific library.
 20. The apparatus of claim 19, wherein the common library includes universal motion and activities acceleration signatures, and the specific library includes person acceleration signatures. 